Open Access
Issue
E3S Web Conf.
Volume 676, 2025
Second Edition International Congress Geomatics in the Service of Land Use Planning (GéoSAT’25)
Article Number 03002
Number of page(s) 20
Section Urban Resilience in the Face of Climate Change
DOI https://doi.org/10.1051/e3sconf/202567603002
Published online 12 December 2025
  1. Intergovernmental Panel on Climate Change, Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (2022). https://www.ipcc.ch/report/ar6/wg2/ [Google Scholar]
  2. M.O. Andreae, Emission of trace gases and aerosols from biomass burning - an updated assessment. Atmos. Chem. Phys. 19, 8523-8546 (2019). https://doi.org/10.5194/acp-19-8523-2019 [Google Scholar]
  3. G.R. Van Der Werf, J.T. Randerson, L. Giglio, T.T. Van Leeuwen, Y. Chen, B.M. Rogers, M. Mu, M.J. Van Marle, D.C. Morton, G.J. Collatz et al., Global fire emissions estimates during 1997-2016. Earth Syst. Sci. Data 9, 697-720 (2017). https://doi.org/10.5194/essd-9-697-2017 [Google Scholar]
  4. C.A. Burton, D.I. Kelley, E. Burke, C. Mathison, C.D. Jones, R.A. Betts, E. Robertson, J.C. Teixeira, M. Cardoso, L.O. Anderson, Fire weakens land carbon sinks before 1.5°C. Nat. Geosci. 17, 1108-1114 (2024). https://doi.org/10.1038/s41561-024-01478-2 [Google Scholar]
  5. W. Schroeder, P. Oliva, L. Giglio, I.A. Csiszar, The new VIIRS 375 m active fire detection data product: Algorithm description and initial assessment. Remote Sens. Environ. 143, 85-96 (2014). https://doi.org/10.1016/j.rse.2013.12.008 [Google Scholar]
  6. E. Chuvieco, J. Lizundia-Loiola, M.L. Pettinari, R. Ramo, M. Padilla, K. Tansey, F. Mouillot, P. Laurent, T. Storm, A. Heil et al., Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies. Earth Syst. Sci. Data 10, 2015-2031 (2018). https://doi.org/10.5194/essd-10-2015-2018 [Google Scholar]
  7. J. Hall, F. Argueta, M. Zubkova, Y. Chen, J. Randerson, L. Giglio, GloCAB: global cropland burned area from mid-2002 to 2020. Earth Syst. Sci. Data 16, 867-881 (2024). https://doi.org/10.5194/essd-16-867-2024 [Google Scholar]
  8. K. Walker, Overcoming common pitfalls to improve the accuracy of crop residue burning measurement based on remote sensing data. Remote Sens. 16, 342 (2024). https://doi.org/10.3390/rs16020342 [Google Scholar]
  9. S. Neiva, J. Silva, A.I. Miranda, NEIVAv1.0: Next-generation emissions inventory expansion of Akagi et. al. (2011) version 1.0. Sci. Total Environ. 946, 174223 (2024). https://doi.org/10.1016/j.scitotenv.2024.174223 [Google Scholar]
  10. T.J. Hawbaker, M.K. Vanderhoof, Y.J.G. Beal, J.D. Takacs, G.L. Schmidt, J.T. Falgout, B. Williams, N.M. Fairaux, M.K. Caldwell, J.J. Picotte et al., Mapping burned areas using dense time-series of Landsat data. Remote Sens. Environ. 198, 504-522 (2017). https://doi.org/10.1016/j.rse.2017.06.027 [Google Scholar]
  11. S. Veraverbeke, P.E. Dennison, I.Z. Gitas, G. Hulley, O.V. Kalashnikova, T. Katagis, L. Kuai, R. Meng, D.A. Roberts, E.N. Stavros, Hyperspectral remote sensing of fire: state-of-the-art and future perspectives. Remote Sens. Environ. 216, 105-121 (2018). https://doi.org/10.1016/j.rse.2018.01.016 [Google Scholar]
  12. A. Smerald, J. Rahimi, C. Scheer, A global dataset for the production and usage of cereal residues in the period 1997-2021. Sci. Data 10, 685 (2023). https://doi.org/10.1038/s41597-023-02587-0 [Google Scholar]
  13. D.A. Jaffe, S.M. O'Neill, N.K. Larkin, A.L. Holder, D.L. Peterson, J.E. Halofsky, A.G. Rappold, Wildfire and prescribed burning impacts on air quality in the United States. J. Air Waste Manag. Assoc. 70, 583-615 (2020). https://doi.org/10.1080/10962247.2020.1749731 [Google Scholar]
  14. Z. Zhu, C.E. Woodcock, Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sens. Environ. 118, 83-94 (2012). https://doi.org/10.1016/j.rse.2011.10.028 [Google Scholar]
  15. F. Li, X. Zhang, S. Kondragunta, I. Csiszar, Comparison of fire radiative power estimates from VIIRS and MODIS observations. J. Geophys. Res. Atmos. 123, 4545-4563 (2018). https://doi.org/10.1029/2017JD027823 [Google Scholar]
  16. S. Hantson, A. Arneth, S.P. Harrison, D.I. Kelley, I.C. Prentice, S.S. Rabin, S. Archibald, F. Mouillot, S.R. Arnold, P. Artaxo et al., The status and challenge of global fire modelling. Biogeosciences 13, 3359-3375 (2016). https://doi.org/10.5194/bg-13-3359-2016 [Google Scholar]
  17. A. Otte, N. Andela, J. Kaiser, S. Lippertz, E. Swinnen, D. van Wees, H. Fargeon, M. Grillakis, A. Tsitsikas, P. Papastefanou et al., A global burned area product for the Copernicus Climate Change Service (C3S): Algorithm, product description and validation. Remote Sens. Environ. 295, 113705 (2023). https://doi.org/10.1016/j.rse.2023.113705 [Google Scholar]
  18. R. Ramo, E. Roteta, I. Bistinas, D. Van Wees, A. Bastarrika, E. Chuvieco, G.R. Van der Werf, African burned area and fire carbon emissions are strongly impacted by small fires undetected by coarse resolution satellite data. Proc. Natl. Acad. Sci. U.S.A. 118, e2011160118 (2021). https://doi.org/10.1073/pnas.2011160118 [Google Scholar]
  19. E. Roteta, A. Bastarrika, M. Padilla, T. Storm, E. Chuvieco, Development of a Sentinel-2 burned area algorithm: Generation of a small fire database for sub-Saharan Africa. Remote Sens. Environ. 222, 1-17 (2019). https://doi.org/10.1016/j.rse.2018.12.011 [Google Scholar]
  20. B. Bai, H. Zhao, S. Zhang, X. Li, X. Zhang, A. Xiu, Forecasting crop residue fires in northeastern China using machine learning. Atmosphere 13, 1616 (2022). https://doi.org/10.3390/atmos13101616 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.